Detection, phenotyping, and quantification of antigen-specific T cells using a peptide-MHC dodecamer
Bibliographic record
Abstract
Here we report a peptide-MHC (pMHC) dodecamer as a "next generation" technology that is a significantly more sensitive and versatile alternative to pMHC tetramers for the detection, isolation, and phenotypic analysis of antigen-specific T cells. In particular, dodecamers are able to detect two- to fivefold more antigen-specific T cells in both human and murine CD4(+)and CD8(+)αβ T-cell compartments compared with the equivalent tetramers. The low-affinity, tetramer-negative, dodecamer-positive T cells showed comparable effector cytokine responses as those of high-affinity, tetramer-positive T cells. Dodecamers are able to detect early stage CD4(+)CD8(+)double-positive thymocytes on which T-cell receptors are 10- to 30-fold less dense than mature T cells. Dodecamers also show utility in the analysis of γδ T cells and in cytometry by time-of-flight applications. This construct has a simple structure with a central scaffold protein linked to four streptavidin molecules, each having three pMHC ligands or other molecules. The dodecamer is straightforward and inexpensive to produce and is compatible with current tetramer technology and commercially available streptavidin conjugates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".